add cpu one hot

mindspore/ccsrc/device/cpu/kernel/one_hot_cpu_kernel.cc

+/**
+ * Copyright 2020 Huawei Technologies Co., Ltd
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#include "device/cpu/kernel/one_hot_cpu_kernel.h"
+#include "device/cpu/cpu_device_address.h"
+
+namespace mindspore {
+namespace device {
+namespace cpu {
+void OneHotCPUKernel::InitKernel(const CNodePtr &kernel_node) {
+  MS_EXCEPTION_IF_NULL(kernel_node);
+  auto output_shape = AnfAlgo::GetOutputInferShape(kernel_node, 0);
+  if (output_shape.size() < 2) {
+    MS_LOG(EXCEPTION) << "invalid output shape size: " << output_shape.size();
+  }
+  int axis = AnfAlgo::GetNodeAttr<int>(kernel_node, AXIS);
+  if (axis != -1 && IntToSize(axis) >= output_shape.size()) {
+    MS_LOG(EXCEPTION) << "invalid axis: " << axis;
+  }
+  if (axis == -1) {
+    axis_ = output_shape.size() - 1;
+  } else {
+    axis_ = IntToSize(axis);
+  }
+  depth_ = output_shape[axis_];
+  stride_ = 1;
+  for (size_t i = axis_ + 1; i < output_shape.size(); ++i) {
+    stride_ *= output_shape[i];
+  }
+}
+
+bool OneHotCPUKernel::Launch(const std::vector<kernel::AddressPtr> &inputs,
+                             const std::vector<kernel::AddressPtr> & /*workspace*/,
+                             const std::vector<kernel::AddressPtr> &outputs) {
+  if (inputs.size() < 3 || outputs.empty()) {
+    MS_LOG(EXCEPTION) << "input or output invalid!";
+  }
+  auto indices = reinterpret_cast<int *>(inputs[0]->addr);
+  auto on_value = reinterpret_cast<float *>(inputs[1]->addr)[0];
+  auto off_value = reinterpret_cast<float *>(inputs[2]->addr)[0];
+  auto output = reinterpret_cast<float *>(outputs[0]->addr);
+  size_t elem_num = inputs[0]->size / sizeof(int);
+
+  for (size_t i = 0; i < elem_num; i++) {
+    size_t stride_num = i / stride_;
+    size_t output_index = stride_num * depth_ * stride_ + i % stride_;
+    size_t index = IntToSize(indices[i]);
+    for (size_t j = 0; j < depth_; j++) {
+      if (index == j) {
+        output[output_index] = on_value;
+      } else {
+        output[output_index] = off_value;
+      }
+      output_index += stride_;
+    }
+  }
+
+  return true;
+}
+}  // namespace cpu
+}  // namespace device
+}  // namespace mindspore

mindspore/ccsrc/device/cpu/kernel/one_hot_cpu_kernel.h

+/**
+ * Copyright 2020 Huawei Technologies Co., Ltd
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#ifndef MINDSPORE_CCSRC_DEVICE_CPU_ONE_HOT_CPU_KERNEL_H_
+#define MINDSPORE_CCSRC_DEVICE_CPU_ONE_HOT_CPU_KERNEL_H_
+#include <vector>
+#include <memory>
+#include "device/cpu/cpu_kernel.h"
+#include "device/cpu/cpu_kernel_factory.h"
+
+namespace mindspore {
+namespace device {
+namespace cpu {
+class OneHotCPUKernel : public CPUKernel {
+ public:
+  OneHotCPUKernel() = default;
+  ~OneHotCPUKernel() override = default;
+
+  void InitKernel(const CNodePtr &kernel_node) override;
+
+  bool Launch(const std::vector<AddressPtr> &inputs, const std::vector<AddressPtr> &workspace,
+              const std::vector<AddressPtr> &outputs) override;
+
+ private:
+  size_t depth_;
+  size_t stride_;
+  size_t axis_;
+};
+
+MS_REG_CPU_KERNEL(OneHot, OneHotCPUKernel);
+}  // namespace cpu
+}  // namespace device
+}  // namespace mindspore
+
+#endif  // MINDSPORE_CCSRC_DEVICE_CPU_ONE_HOT_CPU_KERNEL_H_

mindspore/ccsrc/device/cpu/kernel/reshape_cpu_kernel.h

@@ -35,6 +35,8 @@ class ReshapeCPUKernel : public CPUKernel {
 };
 
 MS_REG_CPU_KERNEL(Reshape, ReshapeCPUKernel);
+MS_REG_CPU_KERNEL(Flatten, ReshapeCPUKernel);
+MS_REG_CPU_KERNEL(ExpandDims, ReshapeCPUKernel);
 }  // namespace cpu
 }  // namespace device
 }  // namespace mindspore

tests/st/ops/cpu/test_one_hot_op.py

+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+
+import pytest
+from mindspore import Tensor
+from mindspore.ops import operations as P
+import mindspore.nn as nn
+from mindspore.common.api import ms_function
+import numpy as np
+import mindspore.context as context
+
+context.set_context(device_target='CPU')
+
+
+class NetOneHot(nn.Cell):
+    def __init__(self):
+        super(NetOneHot, self).__init__()
+        self.on_value = 2.0
+        self.off_value = 3.0
+
+        self.depth_1 = 6
+        self.one_hot_1 = nn.OneHot(-1, self.depth_1, self.on_value, self.off_value)
+
+        self.depth_2 = 4
+        self.one_hot_2 = nn.OneHot(0, self.depth_1, self.on_value, self.off_value)
+        self.one_hot_3 = nn.OneHot(0, self.depth_2, self.on_value, self.off_value)
+        self.one_hot_4 = nn.OneHot(1, self.depth_1, self.on_value, self.off_value)
+
+    @ms_function
+    def construct(self, indices1, indices2, indices3, indices4):
+        return (self.one_hot_1(indices1), self.one_hot_2(indices2),
+                self.one_hot_3(indices3), self.one_hot_4(indices4))
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_gpu_training
+@pytest.mark.env_onecard
+def test_one_hot():
+    one_hot = NetOneHot()
+    indices1 = Tensor(np.array([[0, 1], [4, 5], [2, 6]]).astype(np.int32))
+    indices2 = Tensor(np.array([1, 2, 3]).astype(np.int32))
+    indices3 = Tensor(np.array([[0, 1], [1, 0]]).astype(np.int32))
+    indices4 = Tensor(np.array([[0, 1], [4, 5], [2, 6]]).astype(np.int32))
+    output = one_hot(indices1, indices2, indices3, indices4)
+    expect_0 = np.array([
+        [[2., 3., 3., 3., 3., 3.], [3., 2., 3., 3., 3., 3.]],
+        [[3., 3., 3., 3., 2., 3.], [3., 3., 3., 3., 3., 2.]],
+        [[3., 3., 2., 3., 3., 3.], [3., 3., 3., 3., 3., 3.]]
+    ]).astype(np.float32)
+    expect_1 = np.array([
+        [3., 3., 3.],
+        [2., 3., 3.],
+        [3., 2., 3.],
+        [3., 3., 2.],
+        [3., 3., 3.],
+        [3., 3., 3.]
+    ]).astype(np.float32)
+    expect_2 = np.array([
+        [[2., 3.], [3., 2.]], [[3., 2.], [2., 3.]], [[3., 3.], [3., 3.]],
+        [[3., 3.], [3., 3.]]
+    ]).astype(np.float32)
+    expect_3 = np.array([
+        [[2., 3.], [3., 2.], [3., 3.], [3., 3.], [3., 3.], [3., 3.]],
+        [[3., 3.], [3., 3.], [3., 3.], [3., 3.], [2., 3.], [3., 2.]],
+        [[3., 3.], [3., 3.], [2., 3.], [3., 3.], [3., 3.], [3., 3.]]
+    ]).astype(np.float32)
+    assert (output[0].asnumpy() == expect_0).all()
+    assert (output[1].asnumpy() == expect_1).all()
+    assert (output[2].asnumpy() == expect_2).all()
+    assert (output[3].asnumpy() == expect_3).all()